因子增强树集成
Factor-augmented tree ensembles
- Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出用状态空间法提取的潜平稳因子扩展时间序列回归树的信息集，从两维度推广该类回归树，其集成模型可有效解决宏观金融问题，并以美股波动率和商业周期的领先滞后效应为例验证。
AI中文摘要：
本文提出通过状态空间方法提取的潜平稳因子，扩展时间序列回归树的信息集。该方法从两个维度推广了时间序列回归树：其一，它能够处理存在测量误差、非平稳趋势、季节性及缺失观测值等不规则情况的预测变量；其二，它提供了一种透明的方式，可利用特定领域理论为时间序列回归树提供信息。实证结果表明，这些因子增强树的集成是解决宏观金融问题的可靠方法。本文以美国股票波动率与商业周期之间的领先滞后效应为重点展开说明。
英文摘要：
This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. In doing so, this approach generalises time-series regression trees on two dimensions. First, it allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Second, it gives a transparent way for using domain-specific theory to inform time-series regression trees. Empirically, ensembles of these factor-augmented trees provide a reliable approach for macro-finance problems. This article highlights it focussing on the lead-lag effect between equity volatility and the business cycle in the United States.
